List conversation agents

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnly, idempotent, non-destructive and closed-world behavior. The description adds meaningful backend context beyond that: it names the underlying WS command (`conversation/agent/list`) and states the payload is returned unchanged, telling the agent this is a raw passthrough.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Front-loaded with the purpose, then cleanly organized into 'Use when', 'Not for' and 'Returns' blocks. Every sentence carries routing or behavioral information; no filler.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a zero-parameter, read-only discovery tool with full annotations and an output schema, the description is complete. It even discloses the WS command and passthrough return behavior, which the output schema alone would not make obvious.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The tool takes zero parameters, so there is nothing to document and the baseline is 4. The description correctly implies no input is required.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb and resource ('List conversation agents') and scopes it precisely to a pipeline's conversation step. It also names the sibling engine-list tools it is not, so the agent can distinguish it without opening any schema.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicit 'Use when' clause ties the tool to discovering the `conversation_engine` value needed by `voice_create_pipeline`/`voice_update_pipeline`, and a 'Not for' clause routes STT/TTS/wake-word lookups to the specific alternative tools. Nothing is left to inference.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.